Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership.

Energy absorbing efficiency is a key determinant of a structure's ability to provide mechanical protection and is defined by the amount of energy that can be absorbed prior to stresses increasing to a level that damages the system to be protected. Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures.

Rational Structural Design of Polymer Pens for Energy-Efficient Photoactuation.

Photoactuated pens have emerged as promising tools for expedient, mask-free, and versatile nanomanufacturing. However, the challenge of effectively controlling individual pens in large arrays for high-throughput patterning has been a significant hurdle. In this study, we introduce novel generations of photoactuated pens and explore the impact of pen architecture on photoactuation efficiency and crosstalk through simulations and experiments. By introducing a thermal insulating layer and incorporating an air ap in the architecture design, we have achieved the separation of pens into independent units. This new design allowed for improved control over the actuation behavior of individual pens, markedly reducing the influence of neighboring pens. The results of our research suggest novel applications of photoactive composite films as advanced actuators across diverse fields, including lithography, adaptive optics, and soft robotics.

Bayesian reconstruction of magnetic resonance images using Gaussian processes.

A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and < 0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification. Since the model trained on images of healthy brains could be directly used for predictions in pathological brains without retraining, it shows the inherent transferability of this approach and opens doors to its widespread use.

Screening for electrically conductive defects in thin functional films using electrochemiluminescence.

Multifunctional thin films in energy-related devices often must be electrically insulating where a single nanoscale defect can result in complete device-scale failure. Locating and characterizing such defects presents a fundamental problem where high-resolution imaging methods are needed to find defects, but imaging with high spatial resolution limits the field of view and thus the measurement throughput. Here, we present a novel high-throughput method for detecting sub-micron defects in insulating thin films by leveraging the electrochemiluminescence (ECL) of luminol. Through a systematic study of reagent concentrations, buffers, voltage, and excitation time, we identify optimized conditions under which it is possible to detect sub-micron defects at high-throughput. Extrapolating from the signal to background observed for detecting 440 nm wide lines and 620 nm diameter circles, we estimate the minimum detectable features to be lines as narrow as 2.5 nm in width and pinholes as small as 70 nm in radius. We further explore this method by using it to characterize a nominally insulating poly(phenylene oxide) film and find conductive defects that are cross-correlated with high-resolution atomic force microscopy to provide feedback to synthesis. Given this assay's inherent parallelizability and scalability, it is expected to have a major impact on the automated discovery of multifunctional films.

Quantitative nanopatterning of fg-scale liquids with dip-pen nanolithography.

The ability to precisely pattern nanoscale amounts of liquids is essential for biotechnology and high-throughput chemistry, but controlling fluid flow on these scales is very challenging. Scanning probe lithography methods such as dip-pen nanolithography (DPN) provide a mechanism to write fluids at the nanoscale, but this is an open loop process as methods to provide feedback while patterning sub-pg features have yet to be reported. Here, we demonstrate a novel method for programmably nanopatterning liquid features at the fg-scale through a combination of ultrafast atomic force microscopy probes, the use of spherical tips, and inertial mass sensing. We begin by investigating the required probe properties that would provide sufficient mass responsivity to detect fg-scale mass changes and find ultrafast probes to be capable of this resolution. Further, we attach a spherical bead to the tip of an ultrafast probe as we hypothesize that the spherical tip could hold a drop at its apex which both facilitates interpretation of inertial sensing and maintains a consistent fluid environment for reliable patterning. We experimentally find that sphere-tipped ultrafast probes are capable of reliably patterning hundreds of features in a single experiment. Analyzing the changes in the vibrational resonance frequency during the patterning process, we find that drift in the resonance frequency complicates analysis, but that it can be removed through a systematic correction. Subsequently, we quantitatively study patterning using sphere-tipped ultrafast probes as a function of retraction speed and dwell time to find that the mass of fluid transferred can be modulated by greater than an order of magnitude and that liquid features as small as 6 fg can be patterned and resolved. Taken together, this work addresses a persistent concern in DPN by enabling quantitative feedback for nanopatterning of aL-scale features and lays the foundation for programmably nanopatterning fluids.

A framework for materials informatics education through workshops.

Abstract: The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings. In this article, we discuss the importance of materials informatics education via the lens of these workshops, including details such as learning and implementing specific algorithms, the crucial nuts and bolts of ML, and using competitions to increase interest and participation.

Direct pumping of polar fluids with traveling-wave dielectrophoresis.

Efficiently pumping fluids without moving parts in extremely miniaturized formats is challenging. Here, we propose and numerically explore a new type of fluid pump in which a series of electrodes driven at different phases produce a force directly on the molecules of the fluid. This effect is based on traveling-wave dielectrophoresis (twDEP), which has been observed to drive the motion of colloidal particles. Here, we leverage the time needed for fluid molecules with permanent dipoles to align with the applied field to maintain a phase lag between the applied field and the molecular polarization. While requiring operation in the GHz range, this effect is predicted to be efficient due to its ability to directly drive bulk fluid motion. We begin by establishing the foundational equations for this effect and performing finite element simulations to determine its magnitude in a model geometry. By combining theory and a systematic series of calculations, we validate that twDEP pumps should exhibit a fluid flow that scales as the voltage squared divided by the electrode period and that it should increase with the complex permittivity of the fluid and decrease with increasing viscosity. This results in a general equation that predicts the performance of twDEP pumps. Collectively, these computations provide a blueprint for producing twDEP pumps of polar fluids such as water and ethanol. We conclude by noting that the growing interest in high power microwave technology along with metasurfaces to locally tailor phase could provide a path to realizing twDEP pumps in practice.

Experimental observation of metal-organic framework-polymer interaction forces and intercalation.

We attach a MOF crystallite to an atomic force microscope cantilever to realize a system for rapidly and quantitatively studying the interaction between single-crystal MOFs and polymer films. Using this method, we find evidence of polymer intercalation into MOF pores. This approach can accelerate composite design.

Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering.

This article reviews recent developments in the applications of machine learning, data-driven modeling, transfer learning, and autonomous experimentation for the discovery, design, and optimization of soft and biological materials. The design and engineering of molecules and molecular systems have long been a preoccupation of chemical and biomolecular engineers using a variety of computational and experimental techniques. Increasingly, researchers have looked to emerging and established tools in artificial intelligence and machine learning to integrate with established approaches in chemical science to realize powerful, efficient, and in some cases autonomous platforms for molecular discovery, materials engineering, and process optimization. This review summarizes the basic principles underpinning these techniques and highlights recent successful example applications in autonomous materials discovery, transfer learning, and multi-fidelity active learning.

Electric field induced macroscopic cellular phase of nanoparticles.

A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase that is composed of particle-rich walls and particle-free voids under the collective influence of AC and DC voltages. Systematic study of this phase transition shows that it was the result of electrophoretic assembly into a two-dimensional configuration followed by spinodal decomposition into particle-rich walls and particle-poor cells mediated principally by electrohydrodynamic flow. This mechanistic understanding reveals two characteristics needed for a cellular phase to form, namely (1) a system that is considered two dimensional and (2) short-range attractive, long-range repulsive interparticle interactions. In addition to determining the mechanism underpinning the formation of the cellular phase, this work presents a method to reversibly assemble microscale continuous structures out of nanoscale particles in a manner that may enable the creation of materials that impact diverse fields including energy storage and filtration.

Using simulation to accelerate autonomous experimentation: A case study using mechanics.

Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be predicted analytically or computationally, even imperfect predictions can in principle be useful. Here, we investigate whether imperfect data from simulation can accelerate AE using a case study on the mechanics of additively manufactured structures. Initially, we study resilience, a property that is well-predicted by finite element analysis (FEA), and find that FEA can be used to build a Bayesian prior and experimental data can be integrated using discrepancy modeling to reduce the number of needed experiments ten-fold. Next, we study toughness, a property not well-predicted by FEA and find that FEA can still improve learning by transforming experimental data and guiding experiment selection. These results highlight multiple ways that simulation can improve AE through transfer learning.

Reinforcing Magnetorheological Fluids with Highly Anisotropic 2D Materials.

The front cover artwork is provided by the group of Professor Keith Brown at Boston University. The image shows the magnetorheological fluid in a pressure-driven flow and highlights the length scales of the magnetic particles and highly anisotropic 2D sheets. Read the full text of the Article at 10.1002/cphc.202000948.

Closed-Loop Nanopatterning of Liquids with Dip-Pen Nanolithography.

The ability to reliably manipulate small quantities of liquids is the backbone of high-throughput chemistry, but the continual drive for miniaturization necessitates creativity in how nanoscale samples of liquids are handled. Here, we describe a closed-loop method for patterning liquid samples on pL to sub-fL scales using scanning probe lithography. Specifically, we employ tipless scanning probes and identify liquid properties that enable probe-sample transport that is readily tuned using probe withdrawal speed. Subsequently, we introduce a novel two-harmonic inertial sensing scheme for tracking the mass of liquid on the probe. Finally, this is combined with a fluid mechanics-based iterative control scheme that selects printing conditions to meet a target feature mass to enable closed-loop patterning with better than 1% accuracy and ∼4% precision in terms of mass. Taken together, these advances address a pervasive issue in scanning probe lithography, namely, real-time closed-loop control over patterning, and position scanning probe lithography of liquids as a candidate for the robust nanoscale manipulation of liquids for advanced high-throughput chemistry.

Massively parallel cantilever-free atomic force microscopy.

Resolution and field-of-view often represent a fundamental tradeoff in microscopy. Atomic force microscopy (AFM), in which a cantilevered probe deflects under the influence of local forces as it scans across a substrate, is a key example of this tradeoff with high resolution imaging being largely limited to small areas. Despite the tremendous impact of AFM in fields including materials science, biology, and surface science, the limitation in imaging area has remained a key barrier to studying samples with intricate hierarchical structure. Here, we show that massively parallel AFM with >1000 probes is possible through the combination of a cantilever-free probe architecture and a scalable optical method for detecting probe-sample contact. Specifically, optically reflective conical probes on a comparatively compliant film are found to comprise a distributed optical lever that translates probe motion into an optical signal that provides sub-10 nm vertical precision. The scalability of this approach makes it well suited for imaging applications that require high resolution over large areas.

Reinforcing Magnetorheological Fluids with Highly Anisotropic 2D Materials.

Magnetorheological fluids (MRF) are suspensions of magnetic particles that solidify in the presence of a magnetic field. While non-magnetic additives could improve MRF performance, explorations into such additives have not coalesced into an understanding of their influence, and particularly the role of additive morphology. Here, we explore α-Ni(OH)2 2D sheets, with aspect ratios of ∼25,000, as highly anisotropic MRF additives. Experiments studying pressure-driven flow of an MRF with and without these sheets show that their addition can increase the saturation pressure by as much as 46 %. However, shear-mode rheology reveals that they can also weaken the MRF by inhibiting the chaining of the iron particles at low field strengths and have no effect at higher field strengths. In order to reconcile the strikingly different results, we propose that 2D materials introduce a non-Newtonian handle to modify smart fluids in a manner that depends on the curvature of the shearing strain rate profile. Specifically, we identify a modification to the Buckingham-Reiner model of pressure-driven flow for a Bingham plastic in which the sheets widen the solidified plug. This work highlights the subtle interaction between particles in smart fluids and flows while emphasizing the opportunity for using anisotropy to tune this interaction.

Theory for hierarchical assembly with dielectrophoresis and the role of particle anisotropy.

Nonuniform electric fields cause polarizable particles to move through an effect known as dielectrophoresis (DEP). Additionally, the particles themselves create nonuniform fields due to their induced dipoles. When the nonuniform field of one particle causes another to move, it represents a path to hierarchical assembly termed mutual DEP (mDEP). Anisotropic particles potentially provide further opportunities for assembly through intense and intricate local field profiles. Here, we construct a theoretical framework for describing anisotropic particles as templates for assembly through mDEP by considering the motion of small nanoparticles near larger anisotropic nanoparticles. Using finite element analysis, we study eight particle shapes and compute their field enhancement and polarizability in an orientation-specific manner. Strikingly, we find a more than tenfold enhancement in the field near certain particle shapes, potentially promoting mDEP. To more directly relate the field intensity to the anticipated assembly outcome, we compute the volume experiencing each field enhancement versus particle shape and orientation. Finally, we provide a framework for predicting how mixtures of two distinct particle species will begin to assemble in a manner that allows for the identification of conditions that kinetically bias assembly toward specific hierarchical outcomes.

High-resolution measurement of atomic force microscope cantilever resonance frequency.

The atomic force microscope (AFM) is widely used in a wide range of applications due to its high scanning resolution and diverse scanning modes. In many applications, there is a need for accurate and precise measurement of the vibrational resonance frequency of a cantilever. These frequency shifts can be related to changes in mass of the cantilever arising from, e.g., loss of fluid due to a nanolithography operation. A common method of measuring resonance frequency examines the power spectral density of the free random motion of the cantilever, commonly known as a thermal. While the thermal is capable of reasonable measurement resolution and speed, some applications are sensitive to changes in the resonance frequency of the cantilever, which are small, rapid, or both, and the performance of the thermal does not offer sufficient resolution in frequency or in time. In this work, we describe a method based on a narrow-range frequency sweep to measure the resonance frequency of a vibrational mode of an AFM cantilever and demonstrate it by monitoring the evaporation of glycerol from a cantilever. It can be seamlessly integrated into many commercial AFMs without additional hardware modifications and adapts to cantilevers with a wide range of resonance frequencies. Furthermore, this method can rapidly detect small changes in resonance frequency (with our experiments showing a resolution of ∼0.1 Hz for cantilever resonances ranging from 70 kHz to 300 kHz) at a rate far faster than with a thermal. These attributes are particularly beneficial for techniques such as dip-pen nanolithography.

Stiffness of HIV-1 Mimicking Polymer Nanoparticles Modulates Ganglioside-Mediated Cellular Uptake and Trafficking.

The monosialodihexosylganglioside, GM3, and its binding to CD169 (Siglec-1) have been indicated as key factors in the glycoprotein-independent sequestration of the human immunodeficiency virus-1 (HIV-1) in virus-containing compartments (VCCs) in myeloid cells. Here, lipid-wrapped polymer nanoparticles (NPs) are applied as a virus-mimicking model to characterize the effect of core stiffness on NP uptake and intracellular fate triggered by GM3-CD169 binding in macrophages. GM3-functionalized lipid-wrapped NPs are assembled with poly(lactic-co-glycolic) acid (PLGA) as well as with low and high molecular weight polylactic acid (PLAlMW and PLAhMW) cores. The NPs have an average diameter of 146 ± 17 nm and comparable surface properties defined by the self-assembled lipid layer. Due to differences in the glass transition temperature, the Young's modulus (E) differs substantially under physiological conditions between PLGA (E PLGA = 60 ± 32 MPa), PLAlMW (E PLA lMW = 86 ± 25 MPa), and PLAhMW (E PLA hMW = 1.41 ± 0.67 GPa) NPs. Only the stiff GM3-presenting PLAhMW NPs but not the softer PLGA or PLAlMW NPs avoid a lysosomal pathway and localize in tetraspanin (CD9)-positive compartments that resemble VCCs. These observations suggest that GM3-CD169-induced sequestration of NPs in nonlysosomal compartments is not entirely determined by ligand-receptor interactions but also depends on core stiffness.

Physiologically Relevant Mechanics of Biodegradable Polyester Nanoparticles.

Despite the extensive use of biodegradable polyester nanoparticles for drug delivery, and reports of the strong influence of nanoparticle mechanics on nano-bio interactions, there is a lack of systematic studies on the mechanics of these nanoparticles under physiologically relevant conditions. Here, we report indentation experiments on poly(lactic acid) and poly(lactide-co-glycolide) nanoparticles using atomic force microscopy. While dried nanoparticles were found to be rigid at room temperature, their elastic modulus was found to decrease by as much as 30 fold under simulated physiological conditions (i.e., in water at 37 °C). Differential scanning calorimetry confirms that this softening can be attributed to the glass transition of the nanoparticles. Using a combination of mechanical and thermoanalytical characterization, the plasticizing effects of miniaturization, molecular weight, and immersion in water were investigated. Collectively, these experiments provide insight for experimentalists exploring the relationship between polymer nanoparticle mechanics and in vivo behavior.